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At first glance, the most striking part of the SunRise, a recently redeveloped residential tower in Edmonton, Alberta, is the boldly colored facade, with strips of primary color and a lively mural. Called The Land We Share, the vibrant landscape sketch has sparkled on the skyline since its unveiling this past summer. But the mural is far more than a pretty picture. Covered on all sides in a kind of colored solar panel called BIPV made by Canadian firm Mitrex, the mural and the rest of the structure generate roughly 267 kilowatt hours, enough to cut the buildings carbon emissions in half. Typically, high-rises generate solar power primarily via their rooftops. But thats limiting, says Mitrex founder and CEO Danial Hadizadeh. High-rises are exposed to the sunlight, and we can infuse them with panels at a minimal cost, so why not? he says. [Photo: courtesy Mitrex] A smaller part of the cladding company Clarify, Mitrex (named after the Iranian god of the sun) launched five years ago, after solving some of the unique technical challenges around making these colorful panels work. The panels are safe and easy to hang and can be colored in numerous shades in addition to the standard bluish tint. They have been reformulated to be noncombustible and now are cost competitive with other facade choices. Hadizadeh says that next year the company will introduce a new model thats cost competitive with aluminum cladding, and he hopes to see larger real estate portfolios start coating multiple buildings in the panels to reduce their energy costs. [Photo: courtesy Mitrex] Increasing efficiency, lowering cost, and implementation on all elevations and every aspect of the building, thats where we are going, Hadizadeh says. While it is true that, say, a 10-square-foot section of a vertical array on the side of a skyscraper will generate less energy than a similar-size section on a rooftop panel, due to the latters ability to capture more direct sunlight, its still generating considerably more than an un-panelized facade. There might be some difficulty getting every side of a building to provide adequate generation in a super-dense collection of skyscrapers such as in Midtown Manhattan, but thats a relatively small part of the market. [Photo: courtesy Mitrex] In the case of SunRise, the buildings owner, Avenue Living Asset Management, needed the building upgrade to meet certain carbon emission reduction targets to qualify for retrofit funding, and the Mitrex panel made the project pencil out. In fact, Mitrex panels hang atop whats called the rainscreen, a waterproofing and insulating layer on the facade of the building; not only does this approach create power, but it also improves the buildings overall energy efficiency at the same time. Mitrex projects slated to open next year include a medical center on the University of Toronto campus and a series of high-end residential towers in Dubai.
Category:
E-Commerce
A cozy, neutral sameness defines our era of interior design. Velvet sofas. Bouclé armchairs. All-white living rooms. Beds layered with fluffy faux-fur blankets. Calming sage green kitchen cabinets. You see it in furniture catalogs, social media feeds, perhaps even your own home. And we’ve got algorithms to thank. A decade ago, social platforms shifted from chronological feeds to algorithmic ones, optimized to show users what they were most likely to engage with. As many cultural critics have pointed out, those systems reward what is broadly appealing and shareable. In interiors, that has meant rooms that are soothing and inoffensivebut largely devoid of personality. “Algorithms are a mathematical equation based on the statistical middle,” says Christiane Robbins, a founding partner of architectural firm MAP Studio, who has studied algorithms’ influence on design. “Over time, the middle becomes what everybody thinks they want.” [Photo: Lulu & Georgia] Over time, algorithmic aesthetics begin to feel familiar, then comfortable, then indistinguishable from your own taste. Its subtle, says Sara Sugarman, founder and CEO of Lulu and Georgia, a furniture brand that she launched in 2012, just before algorithms reshaped the internet. Your personal style is influenced by these trends whether you realize it or not. You might decide you like a shade of gray without realizing its because youve seen it hundreds of times. But experts like Katherine Lambert, Robbins’ business partner, believe that change is coming. Consumers are getting tired of the visual sameness all around them. Home brands are realizing that they no longer have a distinct point of view that sets them apart from competitors. “We’re seeing a ‘design resistance’ emerging,” says Lambert. “Designers are rebelling against the algorithm.” Sugarman considers herself a member of this resistance. At Lulu and Georgia, she’s pushing back against algorithm-inspired design across her business. Instead, she’s empowering designers who have a strong point of view to create idiosyncratic pieces that draw the customer in. The majority of the brand’s revenue comes from products that it designs and manufactures itself, allowing it to create an aesthetic that stands out from other brands. [Photo: Lulu & Georgia] This strategy has been good for Lulu and Georgia’s bottom line. The company, which is self-funded and profitable, has been growing at a rate of 30% year over year for the past few years. And customers tend to be loyal, with a repeat rate of more than 50%, which is roughly double the industry standard. Lulu and Georgia offers a glimpse into how the world of mass-market interior design might be changing, as consumers want to break free from AI-generated sameness. The Democratization of Design Sugarman grew up immersed in design. Her grandfather, Louis Sugarman, founded Decorative Carpets in West Hollywood in 1955, catering to elite interior designers. As a child, she spent time in the showroom watching designers create custom pieces for wealthy clients. It was a closed system, where professionals controlled access and defined taste. That began to change in the 2000s, as the internet and social media gave a broader audience access to design inspiration. Mass retailers like Target, Ikea, and Wayfair made it possible to recreate high-end looks at lower prices. Sugarman didnt see this shift as a threat. It was incredible, she says. Design became more accessible, and it helped the industry overall. [Photo: Lulu & Georgia] She launched Lulu and Georgia as a digitally native rug brand before expanding into furniture and decor. But as platforms like Instagram, Pinterest, and later TikTok came to dominate visual culture, Sugarman noticed customers arriving with increasingly fixed ideas of what they wantedlabels like modern, coastal, or traditional that all pointed toward the same neutral, minimalist end point. For Robbins, this convergence makes sense. The rise of algorithmic feeds coincided with years of global upheavalfrom the pandemic to political instability. In uncertain times, people gravitate toward what feels familiar, she says. Sameness offers a subliminal sense of security. Algorithmic Design is Good for Business For home brands, flattened taste is operationally convenient. When consumers want the same sofas, colors, and textures, demand becomes easier to forecast and inventory risk shrinks. Searches for white sofas and bouclé furniture have steadily increased over the past decade, making those products reliable bets. If your business depends on scale and predictability, algorithmic sameness is incredibly efficient, Robbins says. You can optimize your supply chain, minimize risk, and flood the zone with products. [Photo: Lulu & Georgia] But Lambert is seeing signs of fatigue in her conversations with designers and clients. People sense that something is off, even if they cant articulate it yet, she says. Especially in [hotels and restaurants], everything looks interchangeable. Theres a global scroll nowwhere everything looks the same no matter where you are. In response, Sugarman has deliberately pushed back against algorithmic design. Lulu and Georgia does not use any trend-forecasting firms and resists letting past sales data dictate future products. This sets it apart from other furniture retailers. The forecasting agency WGSN has a robust interior design division which many manufacturers and brands (like LG and Knoll) use to decide what to make. Target, for its part, has built its own generative AI-powered forecasting platform called Target Trend Brain. By contrast, Sugarman empowers designers with distinct points of view to create pieces that dont yet exist in the market. Roughly 55% of the company’s revenue comes from products that it has designed and manufactured itself; the remaining 45% comes from products it has curated from other suppliers whose aesthetic fits in to Lulu and Georgia’s. The strategy is bearing fruit. Many of the designer collaborations sell out within days. Some of Lulu & Georgia’s bestsellers over the last few years look very different from the soft neutral styles that dominates our feeds: A red marble dining table with rounded leg, a wooden dining table with perforated holes on the base, dining chairs with unusual shapes cut out on the back. The brand collaborates with interdisciplinary designers including ceramicist Lalese Stamp, architect Ginny Macdonald, lighting designer Eny Lee Parker, textile designer Élan Byrd, and fashion designer Carly Cushnie, encouraging them to design what they genuinely want in their own homeseven if it means making a objects with no track record of selling. Products are often manufactured in small quantities to test demand. [Photo: Lulu & Georgia] One example is a small wooden vanity chair designed by longtime collaborator Sarah Sherman Samuel. Sugarman initially doubted it would sell. Most people dont have vanities anymore, she says. Still, they made a small run. The chair quickly sold out, with customers using it as a sculptural accent in living spaces. As with other furniture retailers, Lulu and Georgia also experiments with color through made-to-order pieces. A sofa designed by Macdonald is available in bold shades like mustard yellow and paprika red, produced only after a customer places an order. The approach allows the brand to test unconventional colors without overcommitting inventory. Sometimes, Sugarman says, those experiments become massive hits. [Photo: Lulu & Georgia] For Robbins and Lambert, this strategy works because it is rooted in specificity. Specificity is the secret sauce that throws off the algorithm, Lambert says. The more cultural, historical, and contextual knowledge you bring in, the harder it is for systems to flatten taste. As algorithmic sameness reaches its limits, they believe consumers will increasingly seek out brands willing to take risks. Were seeing fatigue percolate, Robbins says. I think were approaching a cultural tipping point. Designers who resist the algorithm are going to win.
Category:
E-Commerce
About a year ago, an advertisement caught the attention of Ashleigh Ruane, a PhD student in physics at the University of Cambridge. The ad was simple but unusual: Teach AI about physics. Curious, she clicked. She learned that experts across fieldsfrom physics and finance to healthcare and lawwere now being paid to help train AI models to think, reason, and problem-solve like domain specialists. She applied, was accepted, and now logs about 50 hours a week providing data for Mercor, a platform that connects AI labs with domain experts. Ruane is part of a fast-growing cohort of professionals who are shaping how AI models learn. According to Freelancer, thousands of new AI data training and annotation roles have appeared on their marketplace, with most of the growth taking hold in just the past 18 months. These roles range from highly technical expert tasks, like evaluating complex reasoning or diagnosing model errors, to nuanced judgment calls that large models still struggle with. Were entering a really interesting time period, says Freelancer CEO Matt Barrie. AI models need more and more data. Were seeing professionals from every field in every part of the world taking part in this AI data training work. The trend raises bigger questions: If AI models have already been trained on the open internet and vast corporate datasets, why do they still need human experts? What exactly are these experts doing? And how long will this new kind of work be around? AI has read the whole internet’and still needs real experts Theres a common assumption that todays largest AI models already know everything they need to know. After all, theyve been trained on millions of books, articles, papers, and posts. But industry leaders say domain experts are now more important than ever. Models trained on the entire internet can get you to an 80% answer, but in legal or tax, 80% isnt useful, explains Joel Hron, CTO of Thomson Reuters. Our customers demand a high level of accuracy and trust. Leveraging experts ensures accuracy to the highest degree that we can. Ana Price, vice president of supply at Prolific, which provides human data for AI labs, agrees that experts are becoming even more important as AI models move into regulated, high-stakes domains. The demand for human expertise and domain specific feedback from AI models is growing and growing and growing, says Price. As these models have gotten bigger, the errors are becoming harder to spot. Real expertise is needed to judge the substance of what models are producing, and not just the surface level correctness. In other words, the internet alone is not a substitute for structured professional knowledge. The more organizations rely on AI for serious, high-stakes work, the more they need experts to show models how real professionals think. What expert AI trainers actually do Linda Yu spent the last decade as an investor, deploying $4 billion of investments into technology enabled businesses. She started working with Mercor as an expert contributor a year ago, where typical projects involve coaching AI models to think like an investment professional. My role as a domain expert is to evaluate whether the model response is not just technically correct, but whether the complex reasoning behind the response is accurateincluding assumptions the model made, where it may have overreached, where it missed, and what a better answer would be, shares Yu. The work feels less like training an AI model, and more like mentoring a junior analyst. Experts like Yu say the work varies from project to project, and is being applied across industries from law, medicine, engineering, and beyond. Participants are typically paid hourly$85 per hour on averageand may be asked to evaluate a models reasoning on a technical question, rewrite incorrect answers into correct, step-by-step explanations, and compare multiple model outputs and choose which best reflects real-world practice. The output isnt generic content, but high-fidelity reasoning data designed to shape how AI systems operate. AI interviewers interviewing AI trainers The work requires real expertise, which means AI labs need data from experts who are vetted. To assist with the vetting, some platforms rely on AI interviewers to assess the actual expertise of potential AI trainers. Experts jump on a call, and they interview with AI, says Arsham Ghahramani, founder of Ribbon, an AI interviewer with more than 500 customers, including an AI training data provider who is interviewing more than 15,000 experts a month. Youll likely be asked the best interview questions youve ever been asked. AI interviewers assess experts for signals that would indicate red flags around expertise, like irregular response cadence, whether they respond naturally, and of course, whether they have the required expertise for a given domain. It was actually my first interview with not a real person, says Yu. It scanned my resume and came up with really relevant questions. After each answer, the AI interviewer acted like a real person and summarized what I said and asked a question that was a natural extension of our conversation topic. I was fascinated by the technology. AI now evaluates the humans teaching it, a reflection of just how far people have advanced model capabilities. The ‘last mile of information’ still belongs to humans One of the clearest explanations for why expert data remains essential comes from Mark Quinn, senior director of AI operations at Pearl and former head of Waymo engineering operations. He draws a connection between todays AI challenges and autonomous driving. At Waymo, we worked towards the last mile of autonomous mobility. Now, were working towards the last mile of information, Quinn says. Even though AI systems are being developed to close the last mile of information, the reality is that people may still prefer human expert validation if they need an answer on what to do if their dog ate some chocolate. The metaphor resonates across the industry. Even as models get smarter and larger, theres a world full of edge casessituations that require judgment, ethical reasoning, or domain-specific logic that isnt easily captured in general datasets. Some leaders believe the last mile will shrink but never disappear entirely. Hron of Thomson Reuters notes, The base models still have a long way to go to be truly deep. Expert systems and expert knowledge will help models climb to the next level. Price of Prolific adds, Weve only scratched the surface in terms of what AI can do. Humans are a critical piece of the puzzle, especially in niche domains. In other words, the future isnt about replacing experts. Its about scaling the expertise thats essential to making AI models better and safer. A new kind of knowledge work For Ruane, the physics PhD student, expert data work has become a significant source of income. She recently accepted a full-time position, but notes that her new job will only be 38 hours per weekleaving time to continue contributing to AI training projects. What shes experiencing is quickly becoming common: skilled professionals treating AI training work as a supplemental career path, flexible side hustle, or even full-time job. The work plays an increasingly central role in how AI systems operate. As models get more capable, the value of real-world expertise is being redefined, not diminished. Experts arent just using AI. Theyre teaching it how to reason, think, and act like an expert.
Category:
E-Commerce
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